Abstract

During realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of cortical event dynamics, we investigate how cortical structures generate event representations during narrative perception and how these events are stored to and retrieved from memory. Our data-driven approach allows us to detect event boundaries as shifts between stable patterns of brain activity without relying on stimulus annotations and reveals a nested hierarchy from short events in sensory regions to long events in high-order areas (including angular gyrus and posterior medial cortex), which represent abstract, multimodal situation models. High-order event boundaries are coupled to increases in hippocampal activity, which predict pattern reinstatement during later free recall. These areas also show evidence of anticipatory reinstatement as subjects listen to a familiar narrative. Based on these results, we propose that brain activity is naturally structured into nested events, which form the basis of long-term memory representations. We also extend this approach to studying how event scripts (e.g., for a restaurant meal or traveling through an airport) are represented in the brain, and how they shape memory for narratives that instantiate those scripts.

Biography

In the Norman lab, we use biologically realistic neural network models to explore how the brain gives rise to learning and memory phenomena, and we test these models’ predictions using several different methods, ranging from studies of memory performance in college students, to studies of brain-damaged patients with memory disorders, to neuroimaging studies that record brain activity as people learn and remember.

Currently, students in the lab are using computational models to address questions like: What are the “learning rules” that govern strengthening and weakening of memories in the brain? How do brain oscillations contribute to learning? How does sleep contribute to learning? How can we intentionally forget memories? What are the optimal “search strategies” to use when trying to retrieve memories? We are presently running experiments to test the predictions of our models regarding how memories can be weakened and strengthened, and regarding how subjects strategically cue memory and make decisions on different kinds of memory tests.

With regard to neuroimaging, we are developing (along with other Princeton researchers) new techniques for analyzing distributed patterns of neural activity. Specifically, we are using sophisticated classification (“data mining”) algorithms, applied to fMRI and EEG data, to isolate the neural signatures of specific thoughts and memories. Our goal is to use these new analysis tools to track how mental representations come and go and change over the course of an experiment. For example, these new tools make it possible to determine how subjects are cuing memory (i.e., what information are they using to try to elicit recall) at a particular moment based on brain activity.